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Comparison of imaging based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed

Nejla Ozirmak Lermi1, Max Molina Ayala2, Sharia Hernandez2

  • 1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

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Summary

This study systematically evaluated imaging-based spatial transcriptomics (ST) platforms for tumor microenvironment research. Findings reveal platform-specific differences and highlight probe design

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Area of Science:

  • Oncology
  • Molecular Biology
  • Bioinformatics

Background:

  • Imaging-based spatial transcriptomics (ST) is crucial for understanding tumor biology and microenvironments.
  • Systematic evaluations of commercially available ST platforms are lacking.
  • Controlled experiments are needed to compare ST platform performance.

Purpose of the Study:

  • To systematically compare the performance of leading spatial transcriptomics platforms (CosMx, MERFISH, Xenium).
  • To evaluate ST platforms using lung adenocarcinoma and pleural mesothelioma samples.
  • To provide insights into reliable workflows for spatial profiling and molecular discovery.

Main Methods:

  • Utilized serial 5 µm sections of formalin-fixed, paraffin-embedded tumor samples in tissue microarrays.
  • Compared CosMx, MERFISH, and Xenium (uni/multi-modal) ST platforms.
  • Validated ST data against bulk RNA sequencing, multiplex immunofluorescence, GeoMx, and H&E staining.

Main Results:

  • Identified intricate differences in performance between ST platforms.
  • Demonstrated the critical impact of parameters like probe design on data quality.
  • Showcased objective assessment of automatic cell segmentation and phenotyping, alongside manual evaluation.

Conclusions:

  • Spatial transcriptomics platforms exhibit distinct characteristics impacting data quality and biological insights.
  • Optimization of parameters, particularly probe design, is essential for accurate spatial profiling.
  • The study suggests reliable workflows for advancing spatial biology research in oncology.